DCNN for Machine RUL Prediction using Time-series Data #timeseries #machinelearning #datascience

series uh we are thrilled to be here with you this evening for a session full of action-packed learning I am Deepak Singh best part of the data science team at analytics Vidya for those who have joined us for the first time a brief introduction to the data our sessions the data is a series of webinars conducted by the analytics Vidya and led by the top industry experts it is a fun way to understand the concepts of data science from the leading players into Data Tech domain and as the name suggests it's 1R dedicated to data we are hopeful that these sessions are going to be a great source of enrichment and value-adding for our community members now on to our session today uh which is dcnn for machine rule prediction using time series data Argyle is the remaining time of cycles that the machine is likely to operate without any failure by estimating rural the operator can decide the frequency of schedule maintenance and avoid unplanned downtime in this data are the speaker will explain the rule estimation of a machine using sensor data using realistic multivariate time series data for leveraging the power of deep neural networks in the Hands-On we will also focus on how you can build at bcnn which is deep convolutionary neural networks model for the prediction I hope you are excited to attend this data with us so before we kick things off and I hand it over to our speaker a quick recap of the housekeeping items we are recording this session and the recording will be available on our YouTube channel the link you can find in the chat section please use the Qs section for asking any questions you might have during the session and we will do our best to answer them as the data are progresses or towards the end and lastly we will share a feedback goal towards the end and you all are requested to kindly seal that up before leaving the session now on to our speaker in this session of data art we have jitu Mohan and with us is currently working as a data scientist with IBM system Labs he has been working as an integral part of corporate research division for more than a decade now with expertise in multi-disciplinary research Fields like industrial AI deep learning machine learning NLP kg predictive modeling and time series forecasting reinforcement learning internet of things and product engineering he has built multiple products and VPS povs which were deployed and I generated new business Avenues he is a passionate researcher and has filed four patents as part of his research work over to YouTube viewers to your stage is all yours thanks for all joining uh this uh this specific session and what we will be looking into is the DC and that for machine article uh prediction uh this is actually main uh we will focus on missionary prediction using time series data and how we can actually uh do that or uh how we can do a remaining useful life prediction of a specific machine uh using leveraging the techniques of deep learning this is what we were focused next chart please okay yeah I think payback already mentioned about me so this is a short intro about myself I'm working as a data scientist in IBM labs and um prior to IBM I was with corporate technology and research and Siemens and before that I was with a technology and Innovation uh Bosch so maybe we will not spend too much time on my make I I know you guys are interested to listen to the topic so let's go to the next start quickly so I will share the details LinkedIn details and also and my GitHub profile details also so that you can connect with me and also for the Hands-On we'll be able to use the code also so today agenda is first we will look into the three important things about this specific use case what exactly is the problem statement why the specific problem statement is relevant in industry and how we can actually solve this specific problem statement so what why and how then we will not look into the specific use case itself and a little bit about the data and what kind of data set that we are going to use and then to the time series forecasting multivariate time series forecasting after that and then the AI algorithm that we are going to use a little bit about that and then we will quickly move to the Hands-On pair you will be more excited to experience uh how this is really working and in Hands-On we will be looking into maybe understanding the data and then what kind of pre-processing that we are looking into here and normalizations and how we can prepare the data for dcnn and uh how we can train the model and definitely how we can do the inferencing and also we will discuss on data cells okay so next chart please so first of all what exactly is machine are you remaining useful life is uh as the name suggests a specific machine um when in the operating time the efficiency of that specific machine because of it we are entire and how it is going to reduce so what is the available time right uh now uh till this next service or maybe till the next failure that is what the remaining useful life that we are mentioning here so and specifically you can actually State the test the remaining time a machine is likely to operate before it requires repair or replacement so if you look into the rights at the graph here you can see that on one axis you have time so in this specific case there will be number of Cycles it can be the years or it can be months or it can be days or hours depending on the kind of machine that we are looking into and on the y-axis you have health of that specific machine so as I told you the health of a specific machine is going to reduce over a period of time and eventually that specific machine is going to fail because of some reason it is applicable for a specific component in that specific machine also or that complete emission itself take example about an automotive uh the gasoline car let's take an example okay so it's going to fail uh before failing it is better that we uh do the maintenance so that we are not in the trouble uh at that specific time when it is failing right so but it will be great that if you know this remaining useful life of that specific machine so that you can plan your maintenance so in this specific session what we will be looking into our realistic time series data and using the specific time series data how we can actually uh identify the remaining useful life of that specific machine and how that will be useful next chart please so why the machine rural is relevant so you will come across many machines in your daily life right or if you are a plan manager definitely you will be looking into machines uh or you'll be taking help of machines along with your uh Workforce so it is very important that the availability of that specific machine is confirmed so that your business is actually running smoothly if you are a plant operator one of your machine is failing then it is actually causing an impediment for your product production and the thought would just reduce so that your business is reduced take another example where you might have irritated with this particular machine uh failures or the delays caused by this maintenance are sometimes maintenances so you are waiting in your uh for the next flight at your gate and suddenly this particular red line is coming saying that it is flight is delayed we are irritated and you have to now plan more about the travel and how much time you are going to be you are anxious about that all these things and you know like you the airline company is also um kind of frustrated because they are losing their reputation they are there as overhead costs because of this particular delay is actually coming in on the and uh you know it is actually uh calculated that millions of dollars is actually wasted before because of this particular Airline delays itself so it will be great that if you are able to identify maybe this particular maintenance that you as well in advance so that we are not getting into trouble of uh the untimely maintenance and uh you know the negative Impressions that is actually creating because of this is actually impacting a lot more than the money that you're spending at that time to try and but is it very easy to identify these particular faults it is not actually because this is a complex system what we are looking into at lot of components into that and we cannot do a very frequent maintenance also because these vertical frequent maintenances are going to take a lot of uh dollars from your pocket if you are doing if you are going to the frequent maintenance specifically maybe a flight or Airline definitely we can do some checkpoint there is checkpoint so that it is not getting uh it is not failing at any point of time but at the same time well in advance if you are able to predict something like that as something that we are looking into next art please so how actually we can predict the machine are real there are multiple ways that we can do so one such approach using the data is what we are looking into uh right now that is using AI how we can build a model using the run to failure data and then how we can use this particular model for the predictions uh that is are your estimations this is what we are looking into so now let's understand what exactly is the uh run to failure data so you can see in this particular graph here the lifetime uh of that particular uh aircraft is actually here lifetime is actually the lifetime to the next immediate uh maintenance and mentioning here so you can see the condition indicate a value of this particular aircraft that is the specific jet engine type okay somebody is asking what is rul I think I have mentioned this before uh are you a list nothing but remaining useful life or specific machine so for example in this specific case what you can see these uh dark stars that we have here is uh the maintenance operation or some failures identified in that specific aircraft so the indicator the condition indicator is actually an indicative value of the health of that specific machine right and on the x-axis you have the number of Cycles so one cycle is the complete flight that is take off to land into the next airport that is the complete cycle that we are discussing here so a red line actually shows that one specific such turbocharge set engine this is actually getting uh the the health is actually coming down over a period of time and finally we are here now the question is whether it is going to fail here that is this is the time period that we can actually look into uh till the next maintenance or whether it is going to fail here itself so or maybe somewhere here or maybe somewhere here it we can actually do something so that it is actually very uh we can identify this failure uh here so this specific time duration from here to the failure that is the how many number of cycles that is the remaining useful life that we are we will be indicating here so uh what we will be using is run to failure histories of the specific data set we are going to use and we are using this particular data set we are going to build a model so that we will be able to infer uh whether if given a specific specific data whether it is going to fail or not so next up please now as I told you what we will be looking into is a turbofan jet engine in this specific session the remaining useful life is nothing but the the number of Cycles which is remaining that is the number of flights it is actually remaining till the next maintenance has to happen so if you are able to identify maybe uh 24 uh Cycles are remaining we can do a proper planning for that particular maintenance uh maybe at that time of maybe around 22 or 21 we can plan a maintenance so that we are properly maintaining so it will not get uh maybe get into an untimely on uh maintenance situation which is not favorable now the data set that we are going to use a Starbucks and instant degradation data set which is from NASA so I have given the links and all the links in the references in the GitHub page also and the end of the specific PPT also you will be able to get that now what we will be doing here is uh these specific uh data set what we have it's around is having 21 sensor information so he's um data point is actually a sensor information which is actually recorded So as you know uh sensor information is having the x-axis is the time and y-axis is that particular parameter that means it is a time series information okay so this is the information which is available to you about this 21 sensors definitely the remaining useful life which is actually identified by the expert uh with that specific conditions is also provided there uh since it is an actual data and partly it is some part that is actually simulated also you can see a real sense about how we can actually build a model for a real machine using this particular data set now all these particular engines are of similar type so this is one important thing then we we have to consider when we are considering into remaining useful life because these particular sensor informations that should be uh that that we are recording should be similar definitely the machines that we are actually uh using uh also should be similar otherwise it will unnecessarily complicate your model and you will not be able to uh get a good model so that you can a reliable estimation that's not possible in the specific case uh there is actually huge challenge about the uh recording the specific data also maybe I will keep uh that specific uh challenges and all to the end of this session let's go to the next chart so as I told you the data consists of multiple sensor information sensor information can be the fewer flow or the speed fan speed or maybe the hydraulic related pressure information or maybe temperature information and all these things as you know we don't have to worry about what exactly each individual sensor information is and to build a specific model so we will not go into much detail about this particular sensor information and how it is actually related to maybe a failure and related things so another important thing is that um so there are other multiple other approaches also for the for uh remaining useful life that also may be at the end we can actually uh if we are finding some time we can discuss on this also okay so what you can see on the right side is uh some of the sensor information which is slaughtered so uh you can see there is actually on the uh y-axis that specific sensor uh detail sensor values and the x-axis is the the Cycles which is actually also noted so ah as you know the remaining useful life will be reducing that as a first data point will definitely have the highest uh of that specific data frame uh I will talk in the data frame package because what people be looking into a Time series data and a Time series data means uh we are looking into a series of data points which is actually connected right connected on on the time access so we are considering a wind of this particular data at a particular time and then we are trying to find the article so as I told you uh the remaining useful life of right now will be maybe if it is 24 after maybe couple of flights after that it will definitely come to 222 if we have model this good and your inference was actually good definitely it will be going down so uh what you will see is um the number the remaining is for life is a decreasing number and the Y which is you are nothing but your target value that you try to um predict and uh the other values you have sensor information and apart from this particular sensor information you have some setting values also setting value can be the the kind of flight a process for example uh there are it it was uh the the recording of this particular flight uh degradation the aircraft engine degradation was recorded at the specific region or with some specific uh uh uh parameter for example the altitude can be one of the parameter so at specific altitude it was running and this was recorded and related setting information this can also be coming into settings so uh in this specific data uh what you will have is uh the remaining useful life of around I think 300 or so 200 to 300 flights is available and we will be using that particular information here next chart please it's okay so before going to multivariate time series uh and just look into okay GitHub and uh PPT I will be actually uh giving you these particular details um okay Ariel okay I think LinkedIn information related okay keep me posted uh about your questions I'll be looking into intermittently so that we can uh we cannot test some of these important questions uh then and there itself okay now let's come into multivaria time series data I told you that we are going to use the Samsung formation so one generally uh let's say for example uh if uh if you uh have done a Time series forecasting you know that uh there will be a time series variable will be there and you are trying to forecast something into the future so what we will have here is remaining useful life which we want to predict and multiple sensor information that like which is actually indicated here so that we can actually do the prediction so this is not only for one flight that is for multiple flights it is there that is multiple similar uh Tabo uh jet engine data is actually available so that is indicated and maybe from a sample one to sample n so these information so which is the available for you so each particular uh data set a data sample out of this particular data will contain remaining is for life which is actually decreasing from uh 200 maybe to zero zero means uh it is that that failure happened at that particular time so this is information what we are going to use so in uh in fact you can actually think about the specific data something like this in this particular 3D format where in each papers you have the sensor in from multiple sensor information likewise you have multiple maybe 200 Page notebook something that I entered together papers binded together that is the data the tensor that we are looking into here now uh next chart okay what level of Time series data is available uh I mean minute or hourly okay so as I indicated before the the x-axis what we are looking into will piece the cycle that is each individual cycle that we are looking into so one specific cycle uh that is what will be available individual cycle information maybe we will get into the data related information is available uh that I have I will show you that I think you are more focused about one specific data point we will definitely look into that okay so dcnn which is nothing but the Deep convolutional neural network this is the uh the model that we are going to build and um so there are multiple other approaches again apart from the dcnn which is with which you can actually do the uh estimation that is a modeling you can do uh this is one such approach which just actually works right PC and then actually boxes uh so here what we are trying to do is there is a pattern in the sensor information that we want to connect to um and are your information which is nothing but finally an individual value right so what what is the number of Cycles so uh identifying patterns definitely a convolutionary network is uh good at that so we can generally use it for uh in this particular case also multi-peria time series analysis we will be able to use definitely lstm is also another option but it is more tend to maybe or fitting many of the cases and um so here we will be looking into the dcnn pawn B convolution that we will be looking into why we want deconvolution definitely that also we will actually focus in our implementation okay so um on the right side you can see again the chart which actually indicates the health of the red line is just actually getting at the health and remaining yes for life so assume that you are on the latest observation right now what available data points is there what is available that window before uh this particular data that is uh okay what we will take here is a window of information that means let's assume that we have a specific data set and we have the previous maybe uh 200 data points so maybe 500 data points what we will be looking into is a specific window uh and this window uh the same timestamp for all the specific data that is available for us and how we will be able to use that for our real estimation this is what we will uh use what we will uh do so could you please compare the advantages of RN and CNN for this particular okay so uh so let's say our lstms uh and uh cnns we can both we will be able to use uh for the specific use case and uh advantage of CNN as I indicated before us actually we will be able to um more identify this particular patterns and from this particular signals that we have and a model which is actually using this will be able to uh build using um C convolution neural network and convolution as you know the pattern identification it is good at so it is uh good at this particular case and lstm definitely it is a multivariate Time series time series and lstm uh goes together so lstm is also an option which we can actually experiment which has actually give a good results also but you know we have to be very careful about overfitting in lstm So if you have used um the used real data on using lstmr complex lstm network uh ground up if you are building definitely you might have faced this particular problem that uh the overfitting related issues which is actually prevalent in lstm network but you know there are uh if you are investing time on either of this you will actually get and maybe mix also actually works better but here what we are looking into dcn and conversational Network we will be looking into okay so um next start please so this is the GitHub link you have to go so that we can go through the code and quickly we will be able to look into this so people can even navigate to the specific GitHub page okay classical Ankit was asking maybe uh can we apply arima model like this um arima model uh there are variants of an arima model definitely for the multivariate time series and definitely we can experiment with that but you know the specific problem statement is very difficult to model using an arima or a variant of model for multivarial series okay you can search for a mohan hyphen mg in the GitHub and then please navigate to uh the DC and then a Time series Ariel so uh please okay so uh maybe can you elaborate um I think Bala has asked about uh maybe change Point analysis um okay so I'm Pioneer in the time series my question is change Point analysis what is the change Point analysis in the specific problem that you are looking into can you elaborate on that and I can try to focus on that too okay so uh Deepak can you um navigate to this specific git have breakfast okay just listen I can I can help you also can you go to a browser and then search uh and and that you don't have to essentially copy this I can tell you foreign okay so can you uh type there get uh Mohan hyphen MJ okay Mohan m-o-h-a-n python hyphen hyphen it's not forward slash hyphen uh minus hello yeah yeah uh hyphen uh so not forward slash I said okay yeah space GitHub first one can you go to the repositories and DC and then time series Harry well yeah here you need to open two files okay so first one that's actually the main Dot py thank you below that you can open in a new tab actually I want both of these together yeah yeah and uh turbo Rural ipy and B okay so what I have here is uh an uh notebook which is using the turbo class which is available in the other file main.py files and uh okay you can scroll uh when I tell you okay please please okay so uh what we are doing here is we are uh we are importing that particular class and creating an object out of it so the turbo is the object and we are we have the specific data which is printed below so you can see unit number which is not nothing but that specific data the flight specific number and time cycle which is available uh time cycle is nothing but the timestamps uh this particular data is collected definitely uh the settings are the settings information which is actually I was telling you and S1 to S21 are the uh the data uh the sensor data which is actually available so you can see uh multiple unit numbers will be available on this particular data for example let's say you look into the unique uh unit numbers turbo uh dot train and uh you select the unit numbers and unique you'll be able to see around 100 plus data the unit numbers which is actually available so um okay now what you can do is you can scroll down deeper a little bit okay next thing what we are doing here is definitely we will go to the in detail on the other code also main code also but before that let's look into the data a bit turbo plot is actually plotting this particular data set you can see uh S1 it is actually not 520 data is not varying uh for multiple signals itself so as you know the if there is no variation which is actually coming from a specific signal uh you cannot actually use this particular data for the model right there is no predictability using this particular uh sensor value so which are more which are sensors which you feel that it is not important in this particular case definitely you can skip that skip this particular signals uh from your model training um the data frame that you're making and so that is why we are actually visualizing this particular data definitely not only visualizing we have to go a little bit in depth on into this particular data itself so that proper data frame for training and bcn and it's actually available for you please scroll down so I have multiple data sets and the signal data set which is actually printed here you can see that there is a trend in many of these particular signals which is actually giving you that there are some variations happening that is identifiable on the sensor data are definitely a complex pattern which need to be modeled and um then you can use this particular information for building your model okay so right side there are some data it is not uniform for all sensors but definitely you can generally see the trend that towards the end of that particular uh in that machine we cannot say yeah we can say that it is since it is getting maintained maintained so till the when when it is nearing the maintenance there is variations which is actually coming of the signal which is definitely I identifiable by a neural network okay please scroll down deeper okay you can see some of the data the signals which may not be usable definitely I am plotting few of the signals here uh or I mean from the complete flight data I'm just taking only a sample out of it you can in depth look into all this particular data then you can come to a conclusion whether that is usable or not yeah but yeah in general this is the trend please scroll down please scroll down again yeah okay these are again all the sensor we eat sensor uh different uh flights it is actually getting plotted yeah different rights means it is not one cycle of uh you have to uh so in the machine life cycle it is like a life cycle of that specific machine itself this is what I'm indicating to please scroll down foreign frequency is also important you can see that uh the training data service is actually plotted the first uh you can see multiple um the the training the real frequency which is available and also you can see the uh the training frequency of the which is how the bar chart of the frequency for the test also there are some questions um how can we uh build a health indicator mm-hmm okay so we are able to identify our UL itself directly we don't have to build the health indicator definitely there are methods which is actually available uh to identify Health indicator for example if you are able to identify let's say you can see a trend of exponential decay of on some of the sensor values you if you uh work on mathematical methods definitely you will be able to convert that to a health indicator derived from all this particular sensor information there are a lot of Works which is actually done on this particular area also if you are really uh interested on this I mean serious about this definitely uh try to look into that too okay again Bala is asking can we use this change Point analysis since the data is uh oh change Point analysis I I didn't get that change Point analysis what you are mentioning maybe if you are elaborating a little bit uh that will actually will be great definitely um change um seeing some of the uh time series data the change points are important uh then we can use that uh change points uh to derive maybe better data frames that is something that we can do but in this particular context uh I'm not able to get I think yeah there is two times it is winter okay yeah uh I think Bala is mentioning about uh there is a question uh a detailed uh description about the uh change Point Bala has mentioned so uh what we are mentioning about how we can actually use this uh change as the percentage of the changes how we will be able to yes we can definitely derive this particular information uh also and using this particular data and there are a lot of papers by the way this is actually kind of a golden data set for multivariate time series and a lot of obligations on this in this particular area Okay so uh can you go deeper can you go to the second code the next tab uh no no no not here the second tab that you have opened yeah exactly yeah yeah slightly can you uh Zoom zoom in a bit control plus plus or command shift plus yeah that's fine that's fine very fine okay uh so first you can uh you can see uh the importing libraries pandas definitely not buy and Mark broadly about this means we are importing and also uh pickle for uh dumping the data into the pickle file and also the cycle metrics we are using our Square store and uh mean square error value for that we are importing this particular libraries and then uh what we are doing is can you scroll down a bit yeah so we have this uh for building the convolution neural network we have these particular uh uh Imports so you can see that uh con 1B is used and Max cooling won't be sets we used here maybe I will uh explain why this particular won't be convolution is used and before that let's go a little bit into the data also yes can you store them too uh scroll down okay class table so I have a class here which is indicating uh the which has all the uh the codes for the data manipulation and also the predictions and model building and prediction so that has brought us available here so in the initialization what we are doing is we have uh the data set we are defining the column names for this particular data set and also reading the data so you can see I have defined one of our function which is actually reads the data which is calling directly into in it itself so in the reading itself what we are doing is can install installer down a bit more so what we are doing is uh we are also appending the real information because if you see the data set our data set uh so uh I think John is asking about uh data this particular notebook definitely it is available uh there uh the it is available in the key so please go ahead and clone the repo or Fork it yeah so um okay the train information so the data set if you look into the rule information of the train is available as a separate uh uh file so you have to take that also and merge that along with this particular data so that is why this add Oracle is being used and please scroll down a bit okay A bit more I'm not going in the into each line but that is not the scope of this particular session so plot sensor so you have seen the how we are plotting this particular sensor data so this uh Matlab we are using and then using the backlog we are actually plotting all this particular um uh the sensor data please scroll down okay so uh against a little bit scroll down so this particular plot function is using yeah that's fine that's fine yeah so uh evaluate is actually to uh evaluate evaluation matrix by the way evaluation methods for a multivariate Time series not only maybe rmsc or uh the asker value but for time being we are actually considering these particular values but there are other parameters also we will actually uh consider mean absolute percentage there are related information we will look into and also the learnable how much it is trained and learned related information the sensitivity analysis and all we will do afterwards but yeah this is uh something that as a first step you can actually look into R square value and the r must be value so yeah I'm also building a uh the generalized linear model also here using snap handle snap and metal is actually very good uh libraries which you can use for quick uh the linear models and also for brand of forest models it is very much optimized for multiple architecture so please uh feel free to explore that also so I have used a used snap ml linear equation model to have a reference about this particular uh the prediction definitely you should go ahead with the Ariba models or the for the on the multivariate so that you can actually get uh get that reference values also please scroll down I'm not going again into the this is actually fitting and you will be quite aware about these particular steps okay please pause there okay I can scroll up a little bit Yeah add operating condition now what we are doing is in the data set you can see the setting value so we are going to use this particular setting value setting one two and three and then we are what we are going to do is uh we use this particular information so that with reference to this we are going to uh normalize the data General base actually we normalize this particular data with the the standard and normalization methods itself but what we specifically do here is with response to the settings we try to normalize this is important on multivariate this kind of problem statement that there is some reference there is some operating conditions which is actually varying or something uh on the same machines itself we are saying so there are something there is definitely we have to normalize as per that particular values which is available so try to build something with that reference value otherwise what will happen is at the end of the model building it will not be able to converts to a code point so then you may have to come back and then do some experiment something so if you have tried to do something on this lines and the initial time itself it'll actually will be great okay so what uh can you scroll a bit okay uh condition scalar so this is the implementation of the standard scalar using this particular um uh the settings information which I have mentioned before what we are doing is we are um filtering out with the same um settings values uh all the info the the kind of flights we are actually filtering out and trying to normalize the values uh user using the standard uh uh the standard scalar itself uh but with reference to that particular setting value so you can see there are multiple setting value combinations is available under that we are actually doing the condition scalar okay now can you scroll down like here next is actually exponential smoldering as you know the time series data generally comes with a lot of noise so these high frequency noises may not be useful that much for your predictions so what you generally do is try to do an exponential smoothering and um yeah this also the smoothering uh also if you are looking into it can it it it it itself can be on another session because uh there are different smoothening techniques that we can look into and what is the uh what should we look into so that we are not losing out any information which actually contribute to the prediction right so that is very much important so here we have an exponential smoothening implemented which actually takes care of the height uh High uh frequency noise and let me scroll down yeah next what we have here is a gen generate the training data so in the training data as I told you uh what we have definitely we should have uh uh the starting point and end point and uh of this particular from this particular time series data that we have and we should be able to use this particular starting point endpoint and slide it over the complete uh live that is from start to end of that particular flight that is from the highest value of the article to the lowest value and to all the sensor values and use each particular data frames for our convolution that is what we are going to do here so the step first step is actually to get this particular window with some start value and stock value please scroll down the sequence length what we have to select accordingly so depending on the sequence length this particular window actually varies by the sequence strength means also another hyper parameter data which I have to tune generate a data wrapper so next what we are doing going to do is slide this particular window over the complete data set of that specific flight and then generate multiple data frames keep it take the next slide information that is the next unit that is from start of that particular life cycle of the highest value of the audio of the lowest and then slide again take that information and do this for all uh the data by the way you should be very very careful about shuffling about the data on this data because this is a Time series data you should not actually go ahead and mess up with by shuffling this data points here and there now please scroll down and likewise you have to generate on the labels too labels is pretty simple because you are you have the are real values what you have to maybe take is uh the when it is failing from that and right now what is the value the difference you have to take that is our real value and then you have to just give it as a buy okay and you have a generate label wrapper also which is doing that for all the windows that we are taking please scroll down okay same thing we have to do for test data also uh the general data generator you have to implement but it's a bit slightly different because what uh about we have on the train and test uh the data frames um uh that what we have we are creating a slightly different so maybe few lines of code is here and there it is different maybe you can go through that directly we scroll down so next is uh building this particular model so we are first of all um defining the parameters so that is input shape and the kernel size and drop out and all this particular information and the kind of activation that we are going to use and all this and that's normalization we are going to do all these things we are going to do now uh I'm using what I have done here is uh initially there is actually a convolutionary network at a convolution layer which is accessible you can see x equal to the current bond by the way why can't we have you thought about why can't one B uh generally multivariate time series really you go with uh convolution 1D conversation uh the reason being we don't want to mess up that particular data assume that let's say if you are going to the 2D convolution if I give the data frame in a different order that is the columns are slightly different since convolution is actually taking care of the pattern and so if the both the movements is actually there then you can [Music] um uh you you can actually look into you can uh uh you can focus on the kind of quantity mainly because the other one if you if you uh change the column names and all it will actually will uh the learning will have the problem if you are using the convolution uh so that is why the coming quantity that we are using by the way we can actually use a current 2D also but it is more complicated than this uh but yeah feel free to experiment with that also if you have some better maybe um results uh from uh then please try to share that information too please scroll down and again not going into each individual steps again what I have defined is to convolution layers and then a dense layer and then uh a few convolution layers and that fence layer so that that is complete and the model compile I have given relu uh okay uh the the last uh I have given really because what the what we need is definitely it's a kind uh the real value that you have to get that is why we have to be used and uh mean Square data we have used and all the add-up optimizer that we have used standout by the way you can also experiment with multiple tuning this particular with all this particular multivariate uh uh of for this particular multivariate there are a lot of opportunities for tuning on this particular implementation now you are fitting this particular model and the history you will be able to see the the fitted values uh so just just for the floating purpose that I have done this now model evaluation again we are looking into this couple of values R square value and also the rmsc value so that we will have a idea about how your model actually stand at this point of time okay we can go to uh The Notebook that is the previous tab that we have opened the previous code that we were opening the just just this particular left side the tab itself yeah yeah so please scroll down okay so this is again just uh into what what exactly the shape of the the prepare the data and the dcn and then we are training this particular model for 38 box and we are uh building this particular uh model okay please scroll down uh this is how the the training uh looks like and it is actually reducing and I'm not saying it as a kind of a perfect model which actually there are uh by the way uh better implementation you can actually make using the spare this particular um the approach itself uh you have to maybe Direction Optimizer and maybe you use kind of uh optimization on the the for Optimizer for selecting the hyper parameters and you will be able to get a better result out of it so please try to experiment that and if you need some help please feel free to bring me on LinkedIn also so model summary okay can you scroll up let's stop scroll down okay so this is a model summary uh again I'm not going in depth into each model uh the the conversation layer related things and all maybe we'll spend some time for the question answers also so if you have some specific questions on this particular model land or we can actually discuss but it's uh the convolution layer you can see the first conclusion block zero you can see and after that uh the each block is having a count layer and the batch normalization activation and therefore the Dropout at the end and a Max pooling so like this we are repeating couple of times and then we have a uh for the connector layer at the end I mean a couple of fully connected layers at the end so that we have maybe scroll down a bit okay yeah let's see one that's the first layer and then we have fc2 which is at this uh uh layer and uh you have can you scroll down a bit yeah this is how it actually it looks so around 89 percentage accuracy for the training that we are getting in 80 percentage and again on a Time series uh this number is uh the accuracy number along with that we actually have to look into multiple other factors also to see that how the reliability of this particular model all these things I am not going in depth into that because that will become in another session itself so uh this is where we can also try implementing that and definitely please feel free to bring me uh for any of the any help that you need by by the way this notebook or will run uh for sure and if you're seeing some definitely or some issue definitely I will help you on that and also the main file also you can use any up ID and then just a requirement.x text file you can use with the setup the environment it should run without any problem so how calculator okay so once you have this um so how we can actually calculate your uh maybe uh Rajiv is asking about how we have calculated the rul uh if you are just looking into inferencing on uh inferencing um so we can use the same function uh what I have written in the main file file itself to create maybe let's say take a specific data frame out of this particular data set um and then you can create uh the data frame which is required for this model and pass on to this model uh the predict will be able to give you the prediction directly so I have written that also it will it should be in the model underscore eval it it is inside so individual prediction values for the test uh and all it is actually there it is it is coming I have maybe I am not printed in the notebook if I had asked access then we would have actually born into the code and I have shown that directly um okay yeah Bala I have actually uh used it uh for a linear regression not for to maybe uh use that specific model uh but just to explore that just uh it is uh once the data is there we try out uh with some models so that we get a indicated some figure about this particular data so that is why I have used that is not not for any uh kind of a prediction purpose or anything that is just for our reference so uh yeah you're right maybe since it is a Time series signal not be a good option to go with any of the linear regression which is there in between the code there's a small implementation on that this may not be a good option to cope with that I definitely agree on that so please pause to your question so we try to interact um maybe interest maybe uh maybe five minutes more or maybe 10 minutes depending on the questions and then we can actually close this particular session definitely uh we can look into some of the I think it may be considered with the single Spectrum analysis uh Spectrum I'm uh okay is that a Christian or maybe answer to okay can be even can be used a multi-layer per certain bus first of all on your road similarization and the last time you decide otherwise get into a wrong analysis Center or another algorithm selection better uh yeah definitely the MLP is also another option but um what I can see is uh see a try with them maybe MLP if it is getting without our fitting yeah I'll be interesting to see that particular model too okay uh Swami nothing uh see can you bit elaborate on uh tabular data tabular data means uh see I can consider this as maybe a tabular data yeah this is a tabular data right so it is possible but depends on that particular context uh definitely you can use for example NLP you can use uh that's right similar to this itself so a convolutionary life so convolution neural network and we can use in multiple instances and it's quite strong in that also so uh if you want a reliable models for production ready models if you I'm not saying uh this is a completely tuned production ready model or anything this is actually a reference playground where you can actually try to build a more on top of this so that it is more production ready uh okay uh I don't have an option to unmute you but uh please feel free to bring me an uh LinkedIn and uh we can talk in detail on this okay if that option is available to unmuting please do we can have a short chat now also uh okay so uh Roya I think uh Roy has asked a question uh when to use lstm and when to use CNN so uh definitely it is completely depends on the the data and how it actually works for you but uh if there is a time dependencies we want to capture we try to use lstm a lot and then convolution neural network we use when we uh definitely time dependencies we will since it is a pattern anyway time depends is also a pattern we will be able to see you see and then also here and but uh it is a design choice you have to take at any point of time I don't have a blanket answer for uh the specific question when exactly to use what I have seen is that some of the cases um lstm actually works well and um but with uh more towards or intending to our fit also but uh CNN the if it is working well it actually gives a better model uh we usually yeah Swami uh we you we can use uh for CNN for image data the idea about uh using a image or CNN on an image data is actually we are trying to defend some pattern so I'm trying to identify that pattern and then the fully connected layer is to be capturing uh this connection between this particular pattern for a specific uh object or maybe uh depending on the kind of problem statement that you are looking into but object detection then that is what we are doing but uh see wherever the patterns are existing we will be able to use CNN uh deep convolution neural network we will be able to use and if you are if you build a good architecture definitely that pattern uh you will be able to capture definitely you should have an understanding about the pattern that you're going to look into and uh then you will have a better accuracy for the specific model okay so a lot of questions yeah single yeah as I told you uh another analysis different uh types of analysis also there um for the specific problem statement um what we uh what I can say is uh different methodologies exist uh this is uh the flight or machine Diagnostics is existing from long before a lot of numerical techniques actually Excel standard works very well also I'm not saying that uh and and again I'm not a fan of completely uh building end-to-end uh solution with uh neural networks also there are some specific area we can actually use um uh the what is the classical techniques and combine it with neural network uh if we have yeah lstm okay okay um see in some of the uh why lstm Excel standby if you have CNN exists because uh lstm time dependency capturing is very good so if you see some of the problem statement which cannot be solved with CNN it actually saw gets all better with lstm so but it depends on the data and please feel free to maybe experiment with lstm also uh here okay uh and a post-meter cell I have the results for the lstm2 but I just want to focus more on the uh CNN here that that's why the conversational Network here that's why I have actually uh done with this but yeah you can definitely use with fstm too okay any other question the data uh can you maybe discrete time series uh time series is always continuous uh because um the question is actually uh the use case Dr with the continuous time series data and what if the discrete time series data uh that uh so because it is we call it as time series because there is dependency with respect to time so with a variation uh one axis of variation it's actually value is varying up as uh with the time so that is so that dependency is actually there so a discrete uh Time series okay so then what I suggest is definitely I'll be interested to discuss with you more on this also please feel free to ping me on LinkedIn and definitely will be able to catch up and uh and then we can discuss okay I think uh I think same thing with nepala subramanian also but uh yeah you have questions some of the questions are very very important and married I'd like the Christians also please feel free to ping me uh why do we have multiple layers of CNN so CNN um uh I think there are some CNM sessions also which is there as part of this uh series um CNN uh one specific layer of CNN uh definitely capture some of the um patterns but complex patterns can be uh captured with uh the multiple layers that sort of thing I think uh we have already arrested it with the time okay 60 to 80 minutes I think yeah that's fine now have you I will take up maybe a couple of two more questions and then we can go ahead with closure uh use have you used this model to try and digital thing to the aircraft Industries app yeah digital Tunes yeah uh I have not used it uh what I can see is definitely this is uh this is something that can be used uh uh for enabling a digital thing uh in an aircraft industry yeah on an aircraft definitely um yeah if you have some ideas or something definitely we can work on this I will be happy to help you on that okay what is this particular so uh in NASA data set that's one of the question is what is the sensor feature means S3 so uh in NASA has uh published two data set one of the data set it is a normalized data we will not get to know what exactly each sensor means but uh they have also one more data with these particular detailed information I have also pasted a snapshot of this sensor information there and you can actually use that so about this particular S3 it is Data I don't know exactly what the specific S3 so that it's not out there in the metadata of this particular download uh next question I think we will close with this question what I am interested is data have uh continuous scan machine uh whenever it is in UC the time series data is it yeah definitely that can be actually done uh yeah the scanning it's this kind of scanning machines that is intermittently is being used so what we will do is the uh the ideal time we will consider uh we can it can be concerned only the operating time we will actually take we can do it definitely we can do it only thing is that uh the data frame when we are actually creating right so you have to keep an eye on the uh the uh the data frame so that is a tricky part also in multivariate Time series that many of the time we feel that it is actually working and um but uh in reality uh we might have done some silly mistake on the data and it is not really training also uh you will see the us okay and and simple uh yeah definitely in simple ml for this um uh difficult ml methods you can try experiment and then we can discuss on this end also okay I think uh Deepak um we have a nice session yeah yeah yeah thanks so thanks a lot you do uh on behalf of analytical video I would like to thank you and for your time and for today it's such a wonderful session I'm sure our audience found it insightful and hopefully we can conduct more successions with you in the future like we have to wrap it up now yeah yeah definitely so thanks thank you very much uh to all of us joined the session and also thanks to analytics Vidya at all these uh details should be I think available so links are already with you if you are missing anything definitely feel free to connect with me and Linkedin I'll definitely happy to help you okay and links all links are in the chat section okay guys you can access from there okay then uh thank you very much and uh uh nice to meet you and uh nice to discuss with you and samita then and Bala definitely I am interested to discuss with you please bring me in I think I think uh we are not able to discuss all the questions in detail from your side because of the time limitation okay thanks to all uh bye-bye for now thank you so much bye